Video channel categorization schema

ABSTRACT

Implementations are disclosed for scoring and categorizing a video channel. A method includes receiving category identifiers associated with a plurality of videos in a video channel. The plurality of videos is associated with a plurality of categories specified by the category identifiers. The method further includes receiving ratings of each of the plurality of videos in the video channel. The ratings are based on video use within the video channel. The method further includes generating one or more scores of the video channel in view of the category identifiers and the ratings. Each of the one or more scores is indicative of relevance of the video channel to a respective category specified by the category identifiers.

TECHNICAL FIELD

This disclosure relates to the field of video channel categorization, inparticular, to video channel categorization in view of relevancy of thevideo channel to various categories.

BACKGROUND

Consumers can browse and search the Internet for videos that is ofinterest to them. For example, consumers can use a search engine to finda popular music video. When a search is performed for a particularpopular music video, other videos may appear in the search results thatare not related to and are not particular relevant to the particularmusic video that the user is searching for.

Consumers can also access channels in order to view, download, and/orotherwise access videos. Other types of multimedia content, besidesvideos, may be accessed including audio clips, movie clips, TV clips,and music videos, as well as amateur content such as video blogging,short original videos, pictures, photos, other multimedia content, etc.

SUMMARY

The following presents a simplified summary of various aspects of thisdisclosure in order to provide a basic understanding of such aspects.This summary is not an extensive overview of the disclosure. It isintended to neither identify key or critical elements of the disclosure,nor delineate any scope of the particular implementations of thedisclosure or any scope of the claims. Its sole purpose is to presentsome concepts of the disclosure in a simplified form as a prelude to themore detailed description that is presented later.

In an aspect of the present disclosure, a method includes receivingcategory identifiers associated with a plurality of videos in a videochannel. The plurality of videos is associated with a plurality ofcategories specified by the category identifiers. The method furtherincludes receiving ratings of each of the plurality of videos in thevideo channel. The ratings are based on video use within the videochannel. The method further includes generating one or more scores ofthe video channel in view of the category identifiers and the ratings.Each of the one or more scores is indicative of relevance of the videochannel to a respective category specified by the category identifiers.

In some implementations, the method further includes outputting one ormore of the plurality of categories specified by the categoryidentifiers having a score exceeding a threshold score.

In some implementations, categories specified by the categoryidentifiers comprise one or more of broad categories and narrowcategories.

In some implementations, the ratings are further based on one or more ofa number of subscribers to each of the plurality of videos, a number ofusers uploading each of the plurality of videos, and a number of usersselecting to like each of the plurality of videos.

In some implementations, a video of the plurality of videos is discardedbased on a low rating of the video prior to the generating of the one ormore scores.

In some implementations, the one or more scores of the video channel arefurther generated based on user consumption of the channel. The userconsumption of the channel includes one or more of a number of channelviews, channel-driven watch time, channels subscribers, channel use, andchannel curation events.

In some implementations, one of the ratings is assigned based onanalyzation of text associated with a respective video of the pluralityof videos.

In some implementations, the generating of the one or more scores isbased on a function of a trained fuser model that is generated topredict the one or more scores.

Computing devices for performing the operations of the above describedmethod and the various implementations described herein are disclosed.Computer-readable media that store instructions for performingoperations associated with the above described method and the variousimplementations described herein are also disclosed.

BRIEF DESCRIPTION OF THE DRAWINGS

The present disclosure is illustrated by way of example, and not by wayof limitation, in the figures of the accompanying drawings, in which:

FIG. 1 illustrates an exemplary system architecture in accordance withan implementation of the disclosure;

FIG. 2 is a block diagram illustrating functional components of achannel categorization subsystem in accordance with an implementation ofthe disclosure;

FIG. 3 is a diagram illustrating an exemplary graphical user interfacewindow depicting a channel in accordance with an implementation of thedisclosure;

FIG. 4 is a diagram illustrating a category identification of a channelin accordance with an implementation of the disclosure;

FIG. 5 is flow diagram illustrating a method for rating videos in avideo channel according to an implementation of the disclosure;

FIG. 6 is a flow diagram illustrating a method for categorizing a videochannel according to an implementation of the disclosure;

FIG. 7 is a block diagram illustrating functional components used infuser training according to an implementation of the disclosure;

FIG. 8 is a flow diagram illustrating a method for training a fusermodel to predict categories and respective scores for a channelaccording to an implementation of the disclosure; and

FIG. 9 is a block diagram illustrating an exemplary computer system inaccordance with an implementation of the disclosure.

DETAILED DESCRIPTION

Aspects and implementations of the present disclosure are directed to avideo channel categorization scheme for scoring and categorizing videochannels that provide multimedia content. Implementations are describedfor multimedia content such as audio clips, movie clips, TV clips, andmusic videos, as well as amateur content such as video blogging, shortoriginal videos, pictures, photos, other multimedia content, etc.included a video channel. The video channel is assigned a score for aparticular category and all of the categories and respective scores areoutput for the video channel. Each video channel is assigned a scorebased on the video channel as a whole and based on ratings of themultimedia content included in the channel. The score of the channel fora category can be compared to other scores of other channels alsoassociated with the category to establish a hierarchy of the channelsfor the particular category.

The score assigned to the channel can be based on several factorsincluding metadata associated with the multimedia content provided bythe channel and how users consume the multimedia content. Factorsindicative of how users consume the multimedia content may includechannel-driven watch time, subscriptions, curation events, etc.Channel-driven watch time is the amount of time a user spends on thechannel from the time the user access the channels until the user exitsthe channel. Subscriptions are determined by the amount of userssubscribed to a channel. The subscribed users may receive updates orother information periodically from the channel. Curation events mayinclude “subscribing”, “liking”, “following”, “friending”, and so on.These factors may be calculated and/or saved in the form of cumulativenumbers or averages.

Existing multimedia content providers allow users to search forindividual multimedia content by using, for example, a keyword search orby selection of a multimedia content included within category. Supposethat a user wishes to find videos related to baseball. The user may typethe keyword baseball into a search field or, alternatively, the user mayselect a category entitled baseball to view videos related to baseball.However, the individual multimedia content search cannot be extended togroups of multimedia content. That is, users cannot search for groups ofvideos in a channel or a playlist that is related to baseball.

Implementations of the disclosure address the above deficiencies byscoring and categorizing groups of videos as a whole that are includedin a common channel or playlist. By categorizing the channel orplaylist, the channel or playlist can be searched based on a category.When a user performs a search for channels or playlists, the user isprovided with results of the appropriate channels or playlists based oncategorical scores assigned to the channels or playlists. The scoresprovide a hierarchy of the channels or playlists based on the searchcriteria associated with a category so that higher scored search resultsare provided first before lower scored search results.

Accordingly, described herein in various implementations aretechnologies that score and categorize groups of videos included in achannel or playlist. By scoring and categorizing the channels orplaylists, users can browse and search for the groups of videos in achannel or playlist.

In one implementation, a machine-learned model is built (i.e., trained)and utilized to identify the scores and categories of channels.

Multimedia content may be consumed via the Internet and/or via a mobiledevice application. For brevity and simplicity, an online video (alsohereinafter referred to as a video) is used as an example of amultimedia content throughout this document. However, the teaching ofthe present disclosure is applied to multimedia content generally andcan be applied to various types of multimedia content, including forexample, video, audio, text, images, etc. As used herein, multimediacontent can include an electronic file that can be executed or loadedusing software, firmware or hardware configured to present the digitalmedia item to a category.

FIG. 1 illustrates an exemplary system architecture 100, in accordancewith an implementation of the disclosure. System architecture 100includes client devices 110A-110Z, a network 105, a channelcategorization subsystem 120, and a server 130. Each of the variousdevices of system architecture 100 may be connected to one another vianetwork 105. In one implementation, channel categorization subsystem 120may communicate directly with server 130 or via network 105. In animplementation, channel categorization subsystem 120 is external toserver 130. In another implementation, channel categorization subsystem120 may be a part of server 130. In one implementation, network 105 mayinclude a public network (e.g., the Internet), a private network (e.g.,a local area network (LAN) or wide area network (WAN)), a wired network(e.g., Ethernet network), a wireless network (e.g., an 802.11 network ora Wi-Fi network), a cellular network (e.g., a Long Term Evolution (LTE)network), routers, hubs, switches, server computers, and/or acombination thereof.

In an embodiment, server 130 may be a video sharing platform thatprovides access to videos, etc. The video sharing platform providessharing of videos on the video sharing platform.

In one implementation, client devices 110A-110Z may include one or morecomputing devices such as personal computers (PCs), laptops, mobilephones, smart phones, tablet computers, netbook computers etc. Clientdevices 110A-110Z may also be referred to as “user devices.” Anindividual user may be associated with (e.g., own and/or use) one ormore client devices (e.g., one or more of client devices 110A-110Z).Client devices 110A-110Z may each be owned and utilized by differentusers at different geographical locations. Each client device includes arespective media viewer 111A-111Z. In one implementation, media viewers111A-111Z may be applications that allow users to view images, videos,web pages, documents, etc. For example, media viewers 111A-111Z may beweb browsers that can access, retrieve, present, and/or navigate thevideos (e.g., web pages such as Hyper Text Markup Language (HTML) pages,digital media items, etc.) provided by a web server. Media viewers111A-111Z may render, display, and/or present the videos (e.g., a webpage, a media viewer) to a user. Media viewers 111A-111Z may alsodisplay an embedded media player (e.g., a Flash® player or an HTML5player) that is embedded in a web page (e.g., a web page that mayprovide access to videos). In another example, media viewers 111A-111Zmay be standalone applications that allow users to view digital videos,digital movies, digital photos, digital music, website content, socialmedia updates, electronic books (ebooks), electronic magazines, digitalnewspapers, digital audio books, electronic journals, web blogs, realsimple syndication (RSS) feeds, electronic comic books, softwareapplications, etc. According to aspects of the present disclosure, mediaviewers 111A-111Z may be applications that allow users to view andsearch for channels or playlists that contain multiple videos.

In one implementation, server 130 and channel categorization subsystem120 may be one or more computing devices (such as a rackmount server, arouter computer, a server computer, a personal computer, a mainframecomputer, a laptop computer, a tablet computer, a desktop computer,etc.), data stores (e.g., hard disks, memories, databases), networks,software components, hardware components, or combinations thereof thatmay be suitable for implementing the various features described herein.In some implementations, server 130 can enable media collaborationservices such as audio and/or video conferences (e.g., among users ofclient devices 110A-110Z) using, for example, streaming video or voiceover IP (VoIP) technologies and may be used for personal, entertainment,business, educational or academically-oriented interactions. Server 130may be dedicated to providing video services and/or video hostingservices.

In one implementation, server 130 hosts multiple channels and videosassociated with each of the multiple channels. Server 130 hosts channel300-channel 330. Channel 300 includes video A 302, video B 306, video C308, video D 310, and featured video 314. Channel 330 includes video A352 and video B 362. Other channels and videos than depicted may behosted by server 130.

Users employing client devices 110A-110Z can view channel 300, channel330, and/or any of the videos associated with each channel. Themultimedia viewers 111A-111Z can display (e.g., using a graphical userinterface) a channel and/or videos hosted by server 130.

A channel may include videos available from a common source or videoshaving a common topic, theme, or substance. The videos can be chosen bya user, made available by a user, uploaded by a user, chosen by a videoprovider, chosen by a broadcaster, etc. A channel can be associated withan owner, who is a user(s) that can perform actions on the channel andupload or associated videos (that are to be provided within) thechannel. The owner can provide a brief description and/or keywords(e.g., metadata) associated with the channel. Different activities canbe associated with the channel based on the owner's actions, such as theowner making video available on the channel, the owner selecting (e.g.,liking) videos associated with another channel, the owner commenting onvideos associated with another channel, etc. Users, other than the ownerof the channel, can subscribe to one or more channels in which they areinterested. The concept of “subscribing” is also called a curation eventand may also be referred to as “liking”, “following”, “friending”, andso on.

In an another implementation (not depicted), the channels may be hostedby a separate server and accessible by server 130.

Channel categorization subsystem 120 scores and categorizes the channelshosted by server 130. Channel categorization subsystem 120 analyzes anumber of factors in order to score and categorize the channels. Detailsregarding channel categorization subsystem 120 are described hereinbelow with respect to FIG. 2.

Although server 130 is depicted as hosting channels 300 and 330, server130 may also store playlists created by users, third parties orautomatically. A playlist may include a list of videos that can beplayed (e.g., streamed) in a sequential or shuffled order on the videosharing platform.

Although implementations of the disclosure are discussed in terms ofvideo sharing platforms that provide sharing of videos on the videosharing platform, implementations may also be generally applied to anytype of network (i.e., a social network) providing connections betweenusers. Implementations of the disclosure are not limited to videosharing platforms that provide access to channels and/or channelsubscriptions to users.

In situations in which the systems discussed here collect personalinformation about users, or may make use of personal information, theusers may be provided with an opportunity to control whether the videosharing platform (i.e., server 130) and/or channel categorizationsubsystem 120 collects user information (e.g., information about auser's social network, social actions or activities, profession, auser's preferences, or a user's current location), or to control whetherand/or how to receive video from the video server that may be morerelevant to the user. In addition, certain data may be treated in one ormore ways before it is stored or used, so that personally identifiableinformation is removed. For example, a user's identity may be treated sothat no personally identifiable information can be determined for theuser, or a user's geographic location may be generalized where locationinformation is obtained (such as to a city, ZIP code, or state level),so that a particular location of a user cannot be determined. Thus, theuser may have control over how information is collected about the userand used by the video sharing platform.

FIG. 2 is a block diagram illustrating functional components of achannel categorization subsystem 120 in accordance with animplementation of the disclosure. Channel categorization subsystem 120includes several components (e.g., modules, sub-modules, applications,etc.) that can be executed by one or more processors of channelcategorization subsystem 120. These components may include, for example,a video storage database 202, an extractor 204, a fuser 206, a videoconverter 208, and an annotated channel database 210. More or lesscomponents can be included in channel categorization subsystem 120 toprovide functionality described herein.

Channel categorization subsystem 120 may include a data store 218 suchas a memory (e.g., random access memory), a cache, a drive (e.g., a harddrive), a flash drive, a database system, or another type of componentor device capable of storing data. Data store 218 may also includemultiple storage components (e.g., multiple drives or multipledatabases) that may also span multiple computing devices (e.g., multipleserver computers). In some implementations, data store 218 may becloud-based. One or more of the devices of system architecture 100 mayutilize data store 218 to store public and private data, and data store218 may be configured to provide secure storage for private data.

In the depicted embodiment of FIG. 2, video storage database 202 isshown. Video storage database 202 stores entries that include videos andchannels. Example entry 1 (212) is channel A, John Singer's Channel.Example entry 2 (214) is video A 214. Example entry 3 (216) is video B216. More or less entries than depicted can be included in video storagedatabase 202.

Each of descriptors, videos, channels, playlists, etc. has a categoryidentifier associated therewith. Category identifiers provide one ormore categories that are associated with the video. A categoryidentifier can be created for a channel based on the videos within thatchannel. In the depicted embodiment of FIG. 2, channel A in entry 1(212) does not have a category identifier associated therewith. Fuser206 creates a category identifier for channel A. Additional informationregarding a category identifier for a channel is provided herein belowwith respect to FIG. 4.

Extractor 204 extracts entries that include videos of a video channel(and their respective category identifiers) from video storage database202. Video A in example entry 2 (214) has an associated categoryidentifier. Video B in example entry 3 (216) has an associated categoryidentifier. Each of these category identifiers includes one or morecategories. Extractor 204 extracts the category identifier for eachvideo and based on the category identifier, assigns a respective ratingto each video. In another implementation, category identifiers may bestored external to video storage database 202 and accessible byextractor 204.

In an implementation, extractor 204 may analyze all entries associatedwith a channel. For example, John Singer's music video channel mayinclude one or more videos in the entries. Extractor 204 may assignindividual ratings to each video.

The assigned rating may be based on one or more factors. Extractor 204analyzes the metadata for each video in an entry to determine itsrelevancy to the channel. Extractor 204 may use additional criteria forassigning a rating to an entry. The additional criteria may include howusers utilize the entry. For example, suppose that a video in an entryincluded in a channel is rarely viewed by users. This particular videomay have some associated keywords (e.g., stored as metadata) that do notappropriately characterize the channel. For example, if the channelrelates broadly to automobiles, some keywords that may be associatedwith a majority of popular videos in the channel include automotive,engines, cars, speed, car racing, race car drivers, etc. However, achannel curator might have added the particular video to the channeleven though it is not specifically related to automobiles but has someentertaining value. For example, this video may be a music video that isnot related to automobiles and may be associated with keywords such asmusic, rap, hip-hop, dance music, etc. Therefore, the keywordsassociated with the particular video are not the same as the keywordsthat are associated with other videos of the channel. The particularvideo in the entry is then assigned a lower rating than the other videosof the channel.

In addition to using keywords and other information within metadata of avideo, the content of the video itself can be utilized to determine arating of a video in an entry. For example, the particular video entrycan be for video A 302. Video A 302 is a video of an interview of JohnSinger. Text associated with video A 302 (e.g., closed caption text,other text contained in the video, etc.) may be analyzed by extractor204 in order to assign a rating to video A 302. The text may indicatethat a conversation takes place between people and no music or songs maybe transcribed. In an embodiment, extractor 204 may assign a rating of0.3 out of 1.0 to video A 302, where 0.0 indicates no relevance to thechannel and 1.0 indicates high relevance to the channel. In anembodiment, a video in the channel that is very popular and is oftenviewed by users visiting the channel is relevant to the channel and isthus assigned a higher rating than a video that is not as popular.

Fuser 206 aggregates all of the ratings of all of the videos of achannel. Fuser 206 may discard some videos having ratings that are notwithin a particular range or out of a particular range. For example,fuser 206 may set a particular threshold for a rating as 0.3. Any videorated 3.0 or lower may be discarded by fuser 206. In an implementation,video A 302 may be discarded because its score of 0.3 is the same as thethreshold score of 0.3. After discarding videos that are rated too low,fuser 206 then combines all of the rated videos to generate a score ofthe channel for each category in a category identifier for the channel.

In an embodiment, fuser 206 can be trained using artificial intelligenceto categorize a channel based on information about the channel and thevideos within the channel that are used as input signals to fuser 206.Details regarding fuser training are described herein below with respectto FIGS. 7-8.

Fuser 206 provides the scores of the channel for each category in thecategory identifier to video converter 208. Extractor 204 also providesinformation about the video in the entries to video converter 208. Videoconverter 208 converts the data identifying the channel provided byfuser 206 into a format that is suitable for use by various databases.For example, video converter 208 may convert the data identifying thechannel into a format that is compatible with video search engines, wordsearch engines, etc.

Video converter 208 then provides the data identifying the channel(which is in a proper format) to annotated channel database 210. Thedata identifying the channels is a final score that is assigned to aparticular channel for each category specified by the categoryidentifier. For example, a final score may be assigned to John Singer'smusic video channel as 0.8 on a scale of 0.0 to 1.0 in the category ofmusic, where 0.0 is the lowest ranking and 1.0 is the highest ranking.Scores are also assigned to John Singer's channel in other categoriesspecified by the category identifier.

The annotated channel database 210 then stores the scores associatedwith each channel. When an engine requires the information about thescore of a channel, the score(s) is/are retrieved from the annotatedchannel database 210. A user may then perform a search for a channel andthe search results are based on the scores of channels in variouscategories.

FIG. 3 is a diagram illustrating an exemplary graphical user interfacewindow depicting a channel in accordance with an implementation of thedisclosure. Graphical user interface (GUI) 301 provides for display agraphical representation of John Singer's music video channel. JohnSinger's music video channel provided by GUI 301 in FIG. 3 is agraphical representation of channel 300 in FIG. 1. GUI 301 may includethe title (John Singer's music video channel) 303. GUI 301 may bedisplayed to a user employing client device 110A using multimedia viewer111A. A menu bar 304 provides various links including home, videos,playlists, channels, discussion, about, and search. Each of the menu bar304 items may be clickable links, such as hyperlinks, that display adifferent webpage upon selection. Also included in GUI 301 is asubscribe link 316. The link may textually (and/or graphically) providea number of all of the users subscribed to the channel. In the depictedembodiment, 500,000 subscribers subscribe to John Singer's music videochannel.

GUI 301 also includes a main region and various other regions. In someimplementations, each region can contain, depict, or otherwise presentvideos. The main region may provide a display of a featured video. Inanother implementation, the main region may display a selected video(video A 302, as shown in FIG. 3). Other regions may be included in thechannel that provides access to other videos. A side region may providean area for advertisement video 312. Other videos may be provided in anyof the regions of the channel.

Access to other videos is provided to a user viewing John Singer's musicvideo channel. The videos include video B 306, video C 308, video D 310,and featured video 314. Other videos 318 may also be included withinJohn Singer's music video channel. Each of the additional videos (otherthan video A 302 displayed in the main region) is represented by arespective thumbnail. Each thumbnail may contain a miniaturized versionof video streams of the respective videos, static images (e.g., arepresentative image, a logo, etc.) associated with the respectivevideos, a miniaturized version of the respective video, or combinationsthereof. It should be noted that although the videos displayed by GUI301 are depicted as being rectangular in shape, other shapes may insteadbe displayed (e.g., a circle, a trapezoid, etc.).

GUI 301 also includes a textual representation 320 of a categoryidentifier for channel 300. In another embodiment, the representation ofthe category identifier may be provided in a graphical and/or acombination of a graphical and textual form. Textual representation 320may provide a list of categories that can be relevant to the channel. Abroad category 333 associated the channel is depicted as music. Narrowcategories (332A-332D) associated with the channel include countrymusic, Dreams Dreams Times Three, John Singer, and 2015 country musicgreatest hits. Details regarding how these categories are derived forthe channel are described herein below.

In an embodiment, each of the categories may include hyperlinks. Uponselection of a hyperlink, the category may expand to provide a list ofadditional relevant channels. For example, upon selection of broadcategory 333, a pop-up window external to GUI 301 or window or frameinternal to GUI 301 may provide a list of other channels that arerelevant to the broad category of “music.” The listed channels may bearranged in a hierarchical manner such that the top listed channels aremore relevant to the category of music than the bottom listed channels.Details regarding the scoring of the channels that determines channelrelevancy to a category are described below. The channels in the listmay be represented as hyperlinks. When such a hyperlink is activated, aviewer is presented with a home screen of the relevant channel.

In an implementation, upon selection of a particular video (e.g., videoB 306, video C 308, video D 310) depicted in GUI 301, the particularvideo is provided in the main region (where video A 302 is provided inthe implementation of FIG. 3). The previous video that occupied the mainregion swaps spots with the area the selected video was in prior to theselection (or moves elsewhere). The selection may be made by using adevice such as a mouse, a digital pen, etc., depression of a key,selection made by touching a touch-screen display, or other means ofselection. Once a video is provided in the main region, the video can beplayed either automatically or upon depression of a play icon or symbol322. Once the video is playing, the play icon 322 may toggle to a pauseicon (not depicted). Once the video is paused by selection of the pauseicon, the pause icon may then toggle back to the play icon 322. Othericons may be provided. In the depicted embodiment of FIG. 3, closedcaptioning or other captions may be provided for display within thevideo. For example, video A 302 is a video of an interview of JohnSinger and the caption provides a text of his speech: “Growing up as ayoung boy . . . ”.

In the embodiment depicted in FIG. 3, a user employing client device110A using multimedia viewer 111A to view channel 300 is using a mobiledevice. In another embodiment, the user may employ any other type ofdevice to view John Singer's music video channel.

Category identifiers may be assigned to channels (including groups ofvideos). FIG. 4 is a diagram illustrating a categorization of a channelin accordance with an implementation of the disclosure. In FIG. 4, acategorization 400 of a channel is shown. Multiple videos that comprisechannel 300 have an associated category identifier 402. In animplementation, category identifier 402 is attached to a channelincluding multiple videos by fuser 206. The term “attached to” refers toassociating/linking a category identifier with a video or channel. Thecategory identifier may be in the form of metadata that is associatedwith a video or channel. The category identifier may include multiplecategories that are associated with the video or channel to which thecategory identifier is attached. Category identifier 402 is created byfuser 206. Category identifier 402 may have associated therewith andspecify broad categories 404 or narrow categories 406. Textualrepresentations 320 of the broad and narrow categories are provided inFIG. 3. In an embodiment, broad categories 404 are general categoriesthat can be associated with a channel. Broad categories 404 may includenews, sports, geography, music, art, games, automotive, comedy,animation, documentaries, etc. Broad categories are also referred to astaxonomy classifiers used to classify a channel on a broad basis. Narrowcategories 406 may be a subset of broad categories 404 and are narrowerthan the broad categories. Some examples of narrow categories includepop music, country music, rock music, classical music, tennis, SouthAmerica, impressionistic art, adventure games, etc.

In the embodiment depicted in FIG. 4, for John Singer's music videochannel, broad categories 404 include music. Narrow categories 406include country music, music videos, John Singer's music, popular music,2015 country music greatest hits, etc. Details regarding how thecategories (broad and narrow) specified by the category identifiers areselected for a channel by fuser 206 are described herein.

FIG. 5 is flow diagram illustrating a method 500 for rating videos in avideo channel according to an implementation of the disclosure. Method500 may be performed by processing logic that includes hardware (e.g.,circuitry, dedicated logic, programmable logic, microcode, etc.),software (e.g., instructions run on a processing device to performhardware simulation), or a combination thereof. In one implementation,method 500 may be performed by extractor 204 as described with respectto FIG. 2. In describing the method 500, reference is made to FIG. 2 toillustrate an implementation. It is noted that the example provided inFIG. 2 is meant for illustrative purposes, and is not to be considered alimiting implementation. Further, method 500 illustrates rating videosin a video channel. However, method 500 can be used to rate othermultimedia content that is included in a video channel.

Referring again to FIG. 5, method 500 begins at block 510 when aplurality of videos of a video channel is extracted from a databasewhere each of the plurality of videos comprises a respective categoryidentifier. In FIG. 2, extractor 204 extracts a plurality of videos,channel descriptors, channel titles, etc. stored as entries (i.e.,example entry 1 (212), example entry 2 (214), and example entry 3 (216))from video storage database 202. Each of the videos within the entriescomprises a respective category identifier. The category identifiers mayinclude narrow and broad categories associated with the videos. Thecategory identifiers may be stored within video storage database 202 orelsewhere. The categories in the category identifiers may be categoriesthat are selected by the creator of the channel, where the creatorannotates the channel with certain categories, categories that arederived from keywords contained with the channel or within thedescription (such as an “About Us” section or the title of the channel),categories that are derived from extracted text of the channel, etc.Channel 300 may be associated with descriptors such as the textcontained in the title of the channel that are used to categorize thechannel. However, such categorization may not be accurate or may beincomplete because it is not based on a calculated scheme that utilizesseveral factors in order to property identify appropriate categoriesthat should be associated with the channel. Thus, the descriptorsassociated with the channel may be inappropriately associated with thechannel. For example, certain keywords may be added as metadata by thecreator of the channel. However, the keywords may not properly indicatewhich categories the channel should actually be associated with. In animplementation, the channel within example entry 1 (212) (channel 300)may not be associated with categories. The categories that are to beassociated with channel 300 are determined by fuser 206, as describedbelow with respect to FIG. 6. Example entry 1 (212) may include somebasic information about the channel such as a description of thechannel, a title of the channel, etc. In an embodiment, video storagedatabase 202 contains within example entry 1 (212) a name of thechannel: “John Singer's Music Video Channel.” In an embodiment, thevideos and respective category identifiers are stored within the samedatabase (i.e., video storage database 202). In another embodiment, theentries, the videos or respective category identifiers may be stored inmore than one database.

Referring back to FIG. 5, at block 520, each of a plurality of videos ofthe video channel is rated based on video use within the video channel.Extractor 204 rates videos within entries 2 and 3 based on video usewithin channel 300. Extractor 204 analyzes data relating to video usethat is stored in video storage database 202. In an implementation, thedata may be stored elsewhere and accessible by extractor 204. The datafor a particular channel may include a number of clicks/videos played,an average channel-driven watch-time of the video played, a number ofsubscribers to the video, the number of other videos that areclicked-through prior to and after the video is selected/played, apercentage of the users that visit the channel that access the video,curation versus uploading of videos, searching versus consuming thevideos through feeds, and other information about how users view thevideo within the channel.

Suppose that for the video B 306 that is stored in example entry 2(214), users that visit John Singer's music video channel frequentlyview video B 306. Data about use of video B 306 may indicate, forexample, that a million users clicked on the video, users on averagewatch 3 minutes 20 seconds of the 4 minute video, 80% of all visitors tothe channel view the video, the video is watched as the first video ofthe channel 50% of the time, and the video has 300,000 subscribers. Thisdata may be based on a range of time (e.g., within a week, within amonth, etc.) and may be averaged over the total users. Annotations(metadata) associated with video B 306 and text extracted from video B306 may indicate that video B is a music video entitled “Dreams DreamsTimes Three.” A category identifier associated with video B may includea broad category of music and narrow categories including country music,Dreams Dreams Times Three, John Singer, and 2015 country music greatesthits. The particular video is a popular video within John Singer's musicvideo channel and many people who are interested in channel 300 are alsointerested in video B 306. Therefore, the video is of relevance to thechannel. Based on the analyzed data, extractor 204 rates video B 306 0.8out of 1.0, where 0.0 is not used within/not popular within the videochannel at all and 1.0 indicates that the video is always used within/ispopular within the video channel.

As described above, video A 302 is a video of an interview of JohnSinger. Most users that visit John Singer's channel want to watch JohnSinger's music videos and are not as interested in news about Singer(e.g., Singer's interviews). Therefore, video A 302 may not be aspopular of a video compared to other videos which are Singer's musicvideos. Extractor 204, based on use of video A, determines that video Ais to be assigned a score of 0.3 out of 1.0. A category identifier thatis associated with video A 302 may include a broad category of news andnarrow categories including celebrity interviews, John Singer, and awardwinning journalist interviews.

A similar rating scheme may be used to assign scores to all videosassociated with John Singer's music video channel. Although videos aredescribed in the examples, any type of multimedia content may similarlybe rated.

Referring back to FIG. 5, at block 530, the rating of the plurality ofvideos and the plurality of category identifiers are provided byextractor 204 to fuser 206. Extractor 204 provides all of the videoratings (including both high and low rated video ratings) and all of thecategory identifiers, including broad and narrow categories of all ofthe videos and the channel itself, to fuser 206 for further processing.Extractor 204 may also provide additional information including adescription of the channel and associated metadata (i.e., channeldescriptor) to fuser 206. A channel descriptor can include a title of achannel, a description of a channel, and/or a user-assigned category. Adescription of the channel can be input by a user that creates thechannel or extracted from a web description of a channel (e.g., datathat is included in the about us section of a website), etc. A-userassigned category can be selected by the user that creates the channel.For example, the user may select “music” as a category of the channel.Such information may be packaged together prior to transmission to fuser206. Method 500 then ends.

FIG. 6 is a flow diagram illustrating a method 600 for categorizing avideo channel according to an implementation of the disclosure. Method600 may be performed by processing logic that includes hardware (e.g.,circuitry, dedicated logic, programmable logic, microcode, etc.),software (e.g., instructions run on a processing device to performhardware simulation), or a combination thereof. In one implementation,method 600 may be performed by fuser 206 as described with respect toFIG. 2. In describing the method 600, reference is made to FIG. 2 toillustrate an implementation. It is noted that the example provided inFIG. 2 is meant for illustrative purposes, and is not to be considered alimiting implementation. In an embodiment, the method 600 of FIG. 6starts when the method 500 of FIG. 5 ends.

Referring again to FIG. 6, method 600 begins at block 610 when categoryidentifiers associated with a plurality of videos in a video channel arereceived. In FIG. 2, fuser 206 receives category identifiers associatedwith a plurality of videos in channel 300 from extractor 204. Theplurality of videos is associated with a plurality of categoriesspecified by the category identifiers. Fuser 206 may receive thecategory identifiers that include broad and narrow categories associatedwith each individual video from extractor 204.

Referring again to FIG. 6, at block 620, ratings of each of theplurality of videos in the video channel are received, where the ratingsare based on video use within the video channel. Fuser 206 receivesratings of each of the plurality of videos in channel 300 from extractor204.

In an embodiment, the category identifiers and/or the ratings may bereceived together as a package by fuser 206.

At block 630, one or more videos of the plurality of videos areoptionally discarded based on a low rating. Fuser 206 discards one ormore videos that have a low rating. The settings for a low ratingthreshold can be set (and/or reset) by a user (such as a programmer, forexample). A low rating may be defined by the user as 0.3 or lower withinthe scale of 0.0 to 1.0. For example, fuser 206 may discard video A 302because 0.3 is the cutoff threshold for a low rating. By discardingvideo A 302, fuser 206 also discards the associated category identifiercategories (including all associated broad and narrow categories) andthe associated rating. For example, if one of the narrow categoriesassociated with video A 302 is celebrity interview, then this categoryis discarded due to the low score of video A 302.

In an embodiment, elimination of a low rated video is performed in orderto discount or disregard any video(s) which is not representative of thechannel as a whole. For example, the contents of video A 302 is aninterview, and such content is not representative of John Singer's musicvideos and therefore, does not have a bearing on and is not relevant toJohn Singer's music video channel.

In another embodiment, more than one low rated video may be discarded.In yet another embodiment, it may be determined that no videos are lowrated (and that all videos contained within a channel are relevant tothe channel). If no video scores fall below the cutoff, then no videosor categories specified by the category identifiers are discarded.

At block 640, one or more scores of the video channel are generated inview of the category identifiers and the ratings, where each of the oneor more scores is indicative of relevance of the video channel to arespective category specified by the category identifiers. Fuser 206generates scores of the video channel in view of the categoryidentifiers and the ratings. Each score indicates a relevance of a videochannel to a respective category specified by the category identifiers.Fuser 206 may analyze the broad categories specified by the receivedcategory identifiers and metadata to determine broad and narrowcategories to associate with the channel. Suppose that fuser 206determines a broad category associated with John Singer's music videochannel to be music. Narrow categories may include John Singer, countrymusic, music videos, John Singer's music, popular music, 2015 countrymusic greatest hits, Dreams Dreams Times Three, etc. Each of thesecategories may be based upon individual videos, channel descriptors, orother annotation (metadata) associated with videos within John Singer'schannel.

Fuser 206 may then generate a score of the video channel for eachcategory specified by the category identifiers. Fuser 206 may furtherbase the channel score on how users consume the multimedia contentavailable on the channel. The channel consumption factors include anumber of channel views, channel-driven watch time, channel subscribers,channel use, channel curation events, or other criteria. If one or moreof these user consumption factors are significantly large (i.e., a largenumber of users watch the channel, “like” the channel, or subscribe tothe channel, most users spend a great deal of time browsing the channelbefore exiting the channel, etc.), fuser 206 may assign a high score tochannel based on these consumption factors. If, however, one or more ofthe consumption factors are small, then the channel may be assigned alower score. In an embodiment, a score of a video channel may be usedfor purposes of ranking. The video channel with the highest score may bethe number one video in a particular category followed by the videochannel with the next higher score and so forth. Suppose that JohnSinger's music video channel is assigned a score of 0.8 on a scale of0.0 to 1.0, where 0.0 is the lowest possible score and 1.0 is thehighest possible score, in the broad category of music specified by thecategory identifier. Based on this score, John Singer's music videochannel may be provided within the most popular music video channels inthe music category. In an embodiment, suppose that John Singer hasreleased only one new song in the year 2014. John Singer's music videochannel may be assigned a score of 0.1 in the narrow category of 2014country music greatest hits. Therefore, John Singer's music videochannel may not appear among the top channels in the category of 2014country music greatest hits.

Fuser 206 assigns respective scores to John Singer's music video channelin each category (broad and/or narrow). The categories are specified bythe category identifiers of individual videos that are received by fuser206 from extractor 204 (and are not contained within one of the videosthat are discarded by fuser 206).

At block 650, one or more categories of the category identifier having ascore exceeding a threshold score used to categorize the video channelare output. Fuser 206 outputs to video converter 208 in FIG. 2 allcategories (broad and/or narrow) specified by the category identifiersthat have a score that exceeds a threshold score which are used tocategorize channel 300. The threshold score can be set (and/or reset) bya user (such as a programmer, for example) controlling channelcategorization subsystem 120. The threshold score may be defined by theuser to be less than or equal to a certain number within a scale (e.g.,a scale of 0.0 to 1.0). In an embodiment, the threshold score may bedetermined automatically based on constraints such as the quality ofcategorization (how fine the categorization that is performed is), atotal number of categories in which categorization is performed, orbased on user input. Fuser 206 may determine to discard a category if itdoes not meet a threshold score. For example, fuser 206 may determine todiscard the category of 2014 country music greatest hits because a scoreof 0.1 may not meet a threshold score. Fuser 206 may determine to outputthe following categories for John Singer's music video channel which areused to categorize the channel: music, country music, music videos, JohnSinger's music, popular music, and Dreams Dreams Times Three. Each ofthese categories exceeds the threshold score and is thus output by fuser206. These categories are then combined to create category identifier402 in FIG. 4 and are thus associated with channel 300. Method 600 thenends.

Video converter 208, after receiving data identifying a channel and itsrespective categories, converts the data identifying the videos withinthe channel into a format suitable for viewing by client devices110A-110Z. The converted data identifying the videos within the channeland the categories of the category identifiers (contained withincategory identifier 402) are then stored in annotated channel database210.

The converted data identifying the videos within the channel andassociated category identifiers can then be provided to channelcategorization subsystem 120 (or other servers). The server can use theconverted data identifying the videos and associated categoryidentifiers to rank the channels, improve text and/or video searches,etc.

The category identifiers can be used to separate channel words orphrases that have different meanings. Suppose that a user wishes tosearch for the music band named Chicago. If the user uses an ordinarytext search and types Chicago as the keyword to be searched, the resultsmay include a city in the United States, a movie, or the music band.However, by using category identifiers, the user can sort throughcategories and select Chicago in the category of musicians.

In another embodiment, the user that wishes to find music by the bandnamed Chicago can view links of categories to find the correct Chicago.The user may be provided with a broad category entitled music. Uponclicking on the music link, narrow categories may be shown to the userso that the user can find the Chicago music video channel that he/shewas looking for.

In an embodiment, in order for fuser 206 to improve its scoring scheme,an artificial intelligence training system may be implemented. FIG. 7 isa block diagram illustrating functional components 700 used in fusertraining. In an embodiment, channel categorization subsystem 120includes some of the components 700 (e.g., modules, sub-modules,applications, etc.) that can be executed by one or more processors ofchannel categorization subsystem 120. These components may include, forexample, video storage database 202, extractor 204, fuser 206, trainingchannel categories and scores (such as scores determined by humans)database 702, and a training extractor 704. In an embodiment, videostorage database 202, extractor 204, and fuser 206 are the samecomponents depicted by FIG. 2. Fuser 206 in FIG. 7 includes fusertrainer 706 and fuser model 708. More or less components 700 can beincluded to provide functionality described herein. In anotherembodiment, some or all of components 700 can be accessed by channelcategorization subsystem 120 but may be located external to channelcategorization subsystem 120.

In FIG. 7, extractor 204 extracts entries that include videos and acorresponding channel title, description, etc. which are provided tofuser trainer 706.

Training channel categories and scores database 702 includes channelcategories and scores that are set by people. Humans view channels (andvideos included in the channel) and manually assign categories andassociated scores to the channel. These categories include broad and/ornarrow categories. For example, humans may watch channel 300 anddetermine that John Singer's music video channel is associated with abroad category identifier (i.e., category) of music. Humans may assignan average score to John Singer's music video channel as 0.8 on a scaleof 0.0 to 1.0 in the category of music, where 0.0 is the lowest rankingand 1.0 is the highest ranking. Additional scores may be assigned toadditional categories of the channel. Fuser trainer 706 of fuser 206obtains each of the categories and associated scores from the trainingchannel categories and scores database 702 and uses them to train fusermodel 708.

Fuser trainer 706 builds a machine-learned model (i.e., fuser model 708)to score and categorize any future channels based on how humanscategorized and scored similar previous channels and based on the videoscontained within the channel. Fuser model 708 may be trained by fusertrainer 706 which generates a function that predicts the channelcategories and associated scores for any future channels that are storedin video storage database 202. In an implementation, fuser trainer 706obtains training channel categories and scores from training channelcategories and scores database 702 every time new channelcategories/scores are available. In this way, fuser trainer 706 mayconstantly or periodically be learning and adapting to the way humansperform channel categorization and scoring. Fuser trainer 706 may obtainmany different channel categories and scores from training channelcategories and scores database 702 and determine a pattern between thevarious channels that are rated by humans. Once fuser model 708 has“learned” how to categorize and score channels, it can apply thefunction (i.e., based on the pattern) to any future channel.Specifically, the function is then applied to a channel stored in videostorage database 202 and extracted by extractor 204 and provided tofuser trainer 706 to provide machine calculated categories andassociated scores for the channel. Thus, fuser trainer 706 learns how totrain (or build) fuser model 708 to mimic the mental process ofassigning channel categories and scores to channels in a manner similarto how humans categorize and score a channel. In other words, fusermodel 708 is trained to mimic the way the humans perform thecategorization and scoring of the categories for a channel. Fuser model708 can then predict channel scores and categories so that additionalchannels can be scored without the assistance of humans. The end resultof the training by fuser trainer 706 is to create a function that canpredict channel categories and associated scores.

In an implementation, suppose that human(s) categorize and score channel300. The human scores for each category is stored in training channelcategories and scores database 702 and are provided to fuser trainer706. The human(s) may categorize channel 300 within the broad categoryof music and the narrow categories of country music, and John Singer.The category identifier of music may be scored by the human(s) as 0.8,country music may be scored by the human(s) as 0.5, and John Singer maybe scored by the humans as 1.0. Fuser trainer 706 also obtainsinformation about channel 300 and the videos within the channel as inputsignals from video storage database 202. The information may includebasic information about the channel such as a description of thechannel, a title of the channel, etc. In an implementation, thisinformation is a name of the channel such as “John Singer's music videochannel.” The videos within the channel include video A and video B.These videos each have associated category identifiers that are attachedto respective videos. The category identifiers may be stored withinvideo storage database 202 or elsewhere. Suppose that each of video Aand video B have the following category identifiers: music, countrymusic, and John Singer. Fuser trainer 706 obtains the information aboutthe channel and the videos within the channel to use as input signals tofuser 206. When fuser trainer 706 trains fuser model 708, it observesthat any channel such as channel 300 that has two videos (i.e., video Aand video B) with some particular category identifiers, that humansassign the channel a score of 0.8 in the category of music, 0.5 in thecategory of country music, and 1.0 in the category of John Singer. Thus,future channels having similarities to channel 300 would be categorizedand scored similar to channel 300. Fuser model 708 may assign the humancategorization and scores to channel 300 and perform a similarcategorization and scoring schema for future channels. Thus, fuser model708 outputs a category identifier for channel 300 that includes thecategories of music (having a score of 0.8), country music (having ascore of 0.5), and John Singer (having a score of 1.0).

Details regarding the process of fuser training is described withrespect to FIG. 8.

FIG. 8 is a flow diagram illustrating a method 800 for training a fusermodel to predict categories and respective scores for a channelaccording to an implementation of the disclosure. Training of the fusermodel is also referred to as supervised learning or machine learning.Method 800 may be performed by processing logic that includes hardware(e.g., circuitry, dedicated logic, programmable logic, microcode, etc.),software (e.g., instructions run on a processing device to performhardware simulation), or a combination thereof. In one implementation,method 800 may be performed by fuser 206 (and specifically by fusertrainer 706 and/or fuser model 708) as described with respect to FIG. 7.In describing the method 800, reference is made to FIG. 7 to illustratean implementation. It is noted that the example provided in FIG. 7 ismeant for illustrative purposes, and is not to be considered a limitingimplementation.

Referring again to FIG. 8, method 800 begins at block 810 when trainingchannel categories and associated scores provided by a human for achannel are received. In FIG. 7, fuser trainer 706 receives trainingchannel categories and associated scores provided by a human (stored intraining channel categories and scores database 702) for a channel.Recall that training extractor 704 extracts training channel categoriesand scores that are provided by human(s) for channels from trainingchannel categories and scores database 702.

At block 820, information about the channel and the videos within thechannel are obtained from the video storage database to use as inputsignals to the fuser. As described above with respect to FIG. 2, videostorage database 202 stores information about the channel and the videoswithin the channel. The information about the channel and the videoswithin the channel are obtained by fuser trainer 706 from video storagedatabase 202 to use as input signals to fuser 206.

At block 830, the fuser model is trained using training channelcategories and associated scores provided by humans for a plurality ofchannels and corresponding input signals for the plurality of channelsto generate a function for predicting channel categories and associatedscores for future channels. Referring to FIG. 7, fuser trainer 706trains fuser model 708 using training channel categories and associatedscores that are provided by humans for a plurality of channels andcorresponding input signals for the plurality of channels (stored intraining channel categories and scores database 702) to generate afunction for predicting channel categories and associated scores forfuture channels.

At block 840, a function of the trained fuser model is returned. Fusermodel 708 returns the function for predicting channel categories andassociated scores for future channels. The method then ends.

Fuser model 708 can then obtain any channel from video storage database202 and then output categories and associated scores that channel. Thecategories and associated scores for a channel can then be attachedwithin a category identifier of that channel. Any categories that do nothave a score that meets a threshold score may be discarded by fusermodel 708. Fuser model 708 can then provide the categories andassociated scores of the video channel to video converter 208. Detailsregarding video converter 208 are described above.

In an implementation, as described above, block 640 in FIG. 6 generatesone or more scores of the video channel. The generating of the one ormore scores is based on a function of a trained fuser model (returned inblock 840 of FIG. 8) that is generated to predict the scores.

The present disclosure often references video channels for simplicityand brevity. However, the teaching of the present disclosure can beapplied to various types of grouped multimedia content includingplaylists, grouplets, groups or subsets of videos, etc.

The present disclosure references video channels and playlists includingvideos. However, the scoring schema of fuser 206 is not limited tovideos. Fuser 206 is independent of the type of channel it is rating.Therefore, fuser 206 can score and generate category identifiers fornon-video channels. In FIG. 2, video storage database, extractor 204,video converter 208, and annotated channel database 210 may bevideo-specific and fuser 206 may be independent of videos. Similarly, inFIG. 7, video storage database, extractor 204, training channelcategories and scores database 702, and training extractor 704 may bevideo-specific and fuser 206 (including fuser trainer 706 and fusermodel 708) may be independent of videos. Therefore, fuser 206 (and fusertrainer 706 and fuser model 708) may be adapted to function invideo-specific and non-video specific environments.

In an embodiment, fuser 206 may be used to score a subset of channels(and not all channels). For example, fuser 206 may score only the top50% of channels having the most click-throughs.

In an embodiment, channel 330 in FIG. 1 is entitled “Rock 'N RC Cars.”Channel 330 may include rock music videos and radio control car videos.A central categorization of the channel based on channel metadata, theabout us descriptor, etc., may indicate that the channel is about rockmusic videos and radio control car videos. Thus, the central categoriesinclude rock music videos and radio control car videos. However, therelevance of the channel to one or more categorizes yields a differentresult. Video A 352 is a radio control car video tutorial and video B362 is a famous rock music video. Users that view/consume the videos onchannel 330 are primarily interested in the videos related to radiocontrol videos (such as video A 352) and not the rock music videos.Therefore, based on use of the videos by consumers, channel 330 isassociated with categories including automotive, hobby kits, radiocontrol, and how-to-videos in the category identifier. These categoriesare also referred to as relevant categories. The rock music videos inthe channel are discarded when fuser 206 scores and categorizes thechannel. The rock music videos are referred to as off-topic because theydo not describe an aspect of the channel and are off-the topic of therelevant categories. Therefore, an emphasis is placed on how content isconsumed within the channel by users for the sake of categorizationinstead of analyzing actual raw, unstructured content of the channel.

In an embodiment, if a relevance of the topic for a video channel cannotbe determined (e.g., the channel is in a foreign language, links are notfunctioning, there is not enough information about the channel or topicavailable), then the channel (or some videos within the channel) can becategorizes as “don't know.”

In an embodiment, a channel can be initially be categorized based onit's title, descriptor, etc. as a specialized channel, a broad channel,or a random channel. A specialized channel is related to a single topicand all (or a majority) of the videos contained in the channel arerelated to the single topic. Channel 300 is an example of a specializedchannel. A broad channel is related to a single broad topic but thevideos are not always about the same thing. Channel 330 is an example ofa broad channel. A random channel may include various videos with noemerging topic in common.

Information about the owner of the channel may be evaluated prior toassigning scores and categorizes to the channel. The informationincludes whether the channel's owner is an official channel owner thathas rights to the channel, if the owner has a particular brand, or ifthe owner is an unknown user not linked to anything in particular.

For simplicity of explanation, the various implementations of themethods of this disclosure are depicted and described as a series ofacts. However, acts in accordance with this disclosure can occur invarious orders and/or concurrently, and with other acts not presentedand described herein. Furthermore, not all illustrated acts may berequired to implement the methods in accordance with the disclosedsubject matter. In addition, those skilled in the art will understandand appreciate that the methods could alternatively be represented as aseries of interrelated states via a state diagram or events.Additionally, it should be appreciated that the implementations of themethods disclosed in this specification are capable of being stored onan article of manufacture to facilitate transporting and transferringsuch methods to computing devices. The term “article of manufacture”, asused herein, is intended to encompass a computer program accessible fromany computer-readable device or storage media.

FIG. 9 illustrates a diagrammatic representation of a machine in theexemplary form of a computer system 900 within which a set ofinstructions, for causing the machine to perform any one or more of themethodologies discussed herein, may be executed. In alternativeimplementations, the machine may be connected (e.g., networked) to othermachines in a LAN, an intranet, an extranet, or the Internet. Themachine may operate in the capacity of a server or a client machine inclient-server network environment, or as a peer machine in apeer-to-peer (or distributed) network environment. The machine may be apersonal computer (PC), a tablet PC, a set-top box (STB), a PersonalDigital Assistant (PDA), a cellular telephone, a web appliance, aserver, a network router, switch or bridge, or any machine capable ofexecuting a set of instructions (sequential or otherwise) that specifyactions to be taken by that machine. Further, while only a singlemachine is illustrated, the term “machine” shall also be taken toinclude any collection of machines that individually or jointly executea set (or multiple sets) of instructions to perform any one or more ofthe methodologies discussed herein. Some or all of the components of thecomputer system 900 may be utilized by or illustrative of one or more ofclient devices 110A-110Z, server 130, or channel categorizationsubsystem 120.

The exemplary computer system 900 includes a processing device(processor) 902, a main memory 904 (e.g., read-only memory (ROM), flashmemory, dynamic random access memory (DRAM) such as synchronous DRAM(SDRAM) or Rambus DRAM (RDRAM), etc.), a static memory 906 (e.g., flashmemory, static random access memory (SRAM), etc.), and a data storagedevice 918, which communicate with each other via a bus 908.

Processor 902 represents one or more general-purpose processing devicessuch as a microprocessor, central processing unit, or the like. Moreparticularly, the processor 902 may be a complex instruction setcomputing (CISC) microprocessor, reduced instruction set computing(RISC) microprocessor, very long instruction word (VLIW) microprocessor,or a processor implementing other instruction sets or processorsimplementing a combination of instruction sets. The processor 902 mayalso be one or more special-purpose processing devices such as anapplication specific integrated circuit (ASIC), a field programmablegate array (FPGA), a DSP, network processor, or the like. The processor902 is configured to execute instructions 926 for performing theoperations and steps discussed herein.

The computer system 900 may further include a network interface device922. The computer system 900 also may include a video display unit 910(e.g., a liquid crystal display (LCD), a cathode ray tube (CRT), or atouch screen), an alphanumeric input device 912 (e.g., a keyboard), acursor control device 914 (e.g., a mouse), and a signal generationdevice 920 (e.g., a speaker).

The data storage device 918 may include a computer-readable storagemedium 924 on which is stored one or more sets of instructions 926(e.g., software) embodying any one or more of the methodologies orfunctions described herein. The instructions 926 may also reside,completely or at least partially, within the main memory 904 and/orwithin the processor 902 during execution thereof by the computer system900, the main memory 904 and the processor 902 also constitutingcomputer-readable storage media. The instructions 926 may further betransmitted or received over a network 974 (e.g., network 105) vianetwork interface device 922.

The computer-readable storage medium 924 may also be used to storeinstructions to perform a method for rating videos in a video channel, amethod for categorizing a video channel, and a method of fuser trainingaccording to implementations of the disclosure, as described herein.While the computer-readable storage medium 924 is shown in an exemplaryimplementation to be a single medium, the terms “computer-readablestorage medium” or “machine-readable storage medium” should be taken toinclude a single medium or multiple media (e.g., a centralized ordistributed database, and/or associated caches and servers) that storethe one or more sets of instructions. The terms “computer-readablestorage medium” or “machine-readable storage medium” shall also be takento include any transitory or non-transitory medium that is capable ofstoring, encoding or carrying a set of instructions for execution by themachine and that cause the machine to perform any one or more of themethodologies of the present disclosure. The term “computer-readablestorage medium” shall accordingly be taken to include, but not belimited to, solid-state memories, optical media, and magnetic media.

In the foregoing description, numerous details are set forth. It will beapparent, however, to one of ordinary skill in the art having thebenefit of this disclosure, that the present disclosure may be practicedwithout these specific details. In some instances, well-known structuresand devices are shown in block diagram form, rather than in detail, inorder to avoid obscuring the present disclosure.

Some portions of the detailed description may have been presented interms of algorithms and symbolic representations of operations on databits within a computer memory. These algorithmic descriptions andrepresentations are the means used by those skilled in the dataprocessing arts to most effectively convey the substance of their workto others skilled in the art. An algorithm is herein, and generally,conceived to be a self-consistent sequence of steps leading to a desiredresult. The steps are those requiring physical manipulations of physicalquantities. Usually, though not necessarily, these quantities take theform of electrical or magnetic signals capable of being stored,transferred, combined, compared, and otherwise manipulated. It hasproven convenient at times, principally for reasons of common usage, torefer to these signals as bits, values, elements, symbols, characters,terms, numbers, or the like.

It should be borne in mind, however, that all of these and similar termsare to be associated with the appropriate physical quantities and aremerely convenient labels applied to these quantities. Unlessspecifically stated otherwise as apparent from the following discussion,it is appreciated that throughout the description, discussions utilizingterms such as “receiving”, “transmitting”, “generating”, “adding”,“subtracting”, “inserting”, “removing”, “analyzing”, “determining”,“enabling”, “identifying”, “modifying” or the like, refer to the actionsand processes of a computer system, or similar electronic computingdevice, that manipulates and transforms data represented as physical(e.g., electronic) quantities within the computer system's registers andmemories into other data similarly represented as physical quantitieswithin the computer system memories or registers or other suchinformation storage, transmission or display devices.

The disclosure also relates to an apparatus, device, or system forperforming the operations herein. This apparatus, device, or system maybe specially constructed for the required purposes, or it may include ageneral purpose computer selectively activated or reconfigured by acomputer program stored in the computer. Such a computer program may bestored in a computer- or machine-readable storage medium, such as, butnot limited to, any type of disk including floppy disks, optical disks,compact disk read-only memories (CD-ROMs), and magnetic-optical disks,read-only memories (ROMs), random access memories (RAMs), EPROMs,EEPROMs, magnetic or optical cards, or any type of media suitable forstoring electronic instructions.

The words “example” or “exemplary” are used herein to mean serving as anexample, instance, or illustration. Any aspect or design describedherein as “example” or “exemplary” is not necessarily to be construed aspreferred or advantageous over other aspects or designs. Rather, use ofthe words “example” or “exemplary” is intended to present concepts in aconcrete fashion. As used in this application, the term “or” is intendedto mean an inclusive “or” rather than an exclusive “or”. That is, unlessspecified otherwise, or clear from context, “X includes A or B” isintended to mean any of the natural inclusive permutations. That is, ifX includes A; X includes B; or X includes both A and B, then “X includesA or B” is satisfied under any of the foregoing instances. In addition,the articles “a” and “an” as used in this application and the appendedclaims should generally be construed to mean “one or more” unlessspecified otherwise or clear from context to be directed to a singularform. Reference throughout this specification to “an implementation” or“one implementation” means that a particular feature, structure, orcharacteristic described in connection with the implementation isincluded in at least one implementation. Thus, the appearances of thephrase “an implementation” or “one implementation” in various placesthroughout this specification are not necessarily all referring to thesame implementation.

It is to be understood that the above description is intended to beillustrative, and not restrictive. Many other implementations will beapparent to those of skill in the art upon reading and understanding theabove description. The scope of the disclosure should, therefore, bedetermined with reference to the appended claims, along with the fullscope of equivalents to which such claims are entitled.

1. A method comprising: receiving category identifiers associated with aplurality of videos in a video channel, wherein the plurality of videosare associated with a plurality of categories specified by the categoryidentifiers; receiving, from a plurality of user devices, ratings ofeach of the plurality of videos in the video channel, the ratings basedon video use within the video channel; and generating one or more scoresof the video channel in view of the category identifiers, watch timeassociated with the video channel, and the ratings, wherein each of theone or more scores are indicative of relevance of the video channel to arespective category specified by the category identifiers, wherein thewatch time is from a first point in time a user accesses the videochannel until a second point in time the user exits the video channel.2. The method of claim 1, further comprising: categorizing the videochannel by one or more of the plurality of categories specified by thecategory identifiers, the one or more of the plurality of categorieshaving a score exceeding a threshold score.
 3. The method of claim 1,wherein categories specified by the category identifiers comprise one ormore of broad categories and narrow categories.
 4. The method of claim1, wherein the ratings are further based on one or more of a firstnumber of subscribers to each of the plurality of videos, a secondnumber of users uploading each of the plurality of videos, or a thirdnumber of users selecting to like each of the plurality of videos. 5.The method of claim 1, wherein: the ratings are further based on atleast one of associated keywords, associated text, or video content; themethod further comprises discarding one or more videos of the pluralityof videos that are below a low rating threshold to generate an updatedplurality of videos; and the generating of the one or more scores of thevideo channel is in view of the category identifiers, the watch time,and ratings of the updated plurality of videos.
 6. The method of claim1, wherein the one or more scores of the video channel are furthergenerated based on user consumption of the channel.
 7. The method ofclaim 6, wherein the user consumption of the channel comprises one ormore of a number of channel views, channels subscribers, channel use,and channel curation events.
 8. A system comprising: a memory; and aprocessing device communicatively coupled to the memory, wherein theprocessing device is to: receive category identifiers associated with aplurality of videos in a video channel, wherein the plurality of videosare associated with a plurality of categories specified by the categoryidentifiers; receive, from a plurality of user devices, ratings of eachof the plurality of videos in the video channel, the ratings based onvideo use within the video channel; and generate one or more scores ofthe video channel in view of the category identifiers, watch timeassociated with the video channel, and the ratings, wherein each of theone or more scores are indicative of relevance of the video channel to arespective category specified by the category identifiers, wherein thewatch time is from a first point in time a user accesses the videochannel until a second point in time the user exits the video channel.9. The system of claim 8, wherein the processing device is further to:categorize the video channel by one or more of the plurality ofcategories specified by the category identifiers, the one or more of theplurality of categories having a score exceeding a threshold score. 10.The system of claim 8, wherein categories specified by the categoryidentifiers comprise one or more of broad categories and narrowcategories.
 11. The system of claim 8, wherein the ratings are furtherbased on one or more of a first number of subscribers to each of theplurality of videos, a second number of users uploading each of theplurality of videos, or a third number of users selecting to like eachof the plurality of videos.
 12. The system of claim 8, wherein: theratings are further based on at least one of associated keywords,associated text, or video content; the processing device is further todiscard one or more videos of the plurality of videos that are below alow rating threshold to generate an updated plurality of videos; and thegenerating of the one or more scores of the video channel is in view ofthe category identifiers, the watch time, and ratings of the updatedplurality of videos.
 13. The system of claim 8, wherein the one or morescores of the video channel are further generated based on userconsumption of the channel.
 14. The system of claim 13, wherein the userconsumption of the channel comprises one or more of a number of channelviews, channels subscribers, channel use, and channel curation events.15. A non-transitory computer-readable storage medium storinginstructions which, when executed, cause a processing device to performoperations comprising: receiving category identifiers associated with aplurality of videos in a video channel, wherein the plurality of videosare associated with a plurality of categories specified by the categoryidentifiers; receiving, from a plurality of user devices, ratings ofeach of the plurality of videos in the video channel, the ratings basedon video use within the video channel; and generating one or more scoresof the video channel in view of the category identifiers, watch timeassociated with the video channel, and the ratings, wherein each of theone or more scores are indicative of relevance of the video channel to arespective category specified by the category identifiers, wherein thewatch time is from a first point in time a user accesses the videochannel until a second point in time the user exits the video channel.16. The non-transitory computer-readable storage medium of claim 15,wherein the operations further comprise: categorizing the video channelby one or more of the plurality of categories specified by the categoryidentifiers having a score exceeding a threshold score.
 17. Thenon-transitory computer-readable storage medium of claim 15, whereincategories specified by the category identifiers comprise one or more ofbroad categories and narrow categories.
 18. The non-transitorycomputer-readable storage medium of claim 15, wherein the ratings arefurther based on one or more of a first number of subscribers to each ofthe plurality of videos, a second number of users uploading each of theplurality of videos, or a third number of users selecting to like eachof the plurality of videos.
 19. The non-transitory computer-readablestorage medium of claim 15, wherein: the ratings are further based on atleast one of associated keywords, associated text, or video content; theoperations further comprise discarding one or more videos of theplurality of videos that are below a low rating threshold to generate anupdated plurality of videos; and the generating of the one or morescores of the video channel is in view of the category identifiers, thewatch time, and ratings of the updated plurality of videos.
 20. Thenon-transitory computer-readable storage medium of claim 15, wherein theone or more scores of the video channel are further generated based onuser consumption of the channel.